Identifying pleiotropic genes for major psychiatric disorders with GWAS summary statistics using multivariate adaptive association tests. (November 2022) - Wang, Yuping; Yang, Yongli; Jia, Xiaocan; Zhao, Chenyu; Yang, Chaojun; Fan, Jingwen; Wu, Meina; Yu, Mengdie; Dong, Ani; Wang, Nana; Lian, Jiao; Shi, Xuezhong
Abstract: Background: Genome wide association studies (GWAS) have discovered a few of single nucleotide polymorphisms (SNPs) related to major psychiatric disorders. However, it is not completely clear which genes play a pleiotropic role in multiple disorders. The study aimed to identify the pleiotropic genes across five psychiatric disorders using multivariate adaptive association tests. Methods: Summary statistics of five psychiatric disorders were downloaded from Psychiatric Genomics Consortium. We applied linkage disequilibrium score regression (LDSC) to estimate genetic correlation and conducted tissue and cell type specificity analyses based on Multi-marker Analysis of GenoMic Annotation (MAGMA). Then, we identified the pleiotropic genes using MTaSPUsSet and aSPUs tests. We ultimately performed the functional analysis for pleiotropic genes. Results: We confirmed the significant genetic correlation and brain tissue and neuron specificity among five disorders. 100 pleiotropic genes were detected to be significantly associated with five psychiatric disorders, of which 55 were novel genes. These genes were functionally enriched in neuron differentiation and synaptic transmission. Limitations: The effect direction of pleiotropic genes couldn't be distinguished due to without individual-level data. Conclusion: We identified pleiotropic genes using multivariate adaptive association tests and explored their biological function. The findings may provide novel insight into the development and implementation of prevention and treatment as well as targeted drug discovery in practice. Highlights: The MTaSPUsSet, a novel adaptive association test, considers association patterns. More pleiotropic genes were found by aggregating multiple related phenotypes. It is a cost-effective study based on GWAS summary statistics.